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# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from itertools import chain
from numbers import Integral
import numpy as np
import scipy.sparse as sp
from sklearn.utils import metadata_routing
from ..base import BaseEstimator, TransformerMixin, _fit_context
from ..utils._param_validation import Interval, StrOptions
from ._hashing_fast import transform as _hashing_transform
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if hasattr(d, "iteritems") else d.items()
class FeatureHasher(TransformerMixin, BaseEstimator):
"""Implements feature hashing, aka the hashing trick.
This class turns sequences of symbolic feature names (strings) into
scipy.sparse matrices, using a hash function to compute the matrix column
corresponding to a name. The hash function employed is the signed 32-bit
version of Murmurhash3.
Feature names of type byte string are used as-is. Unicode strings are
converted to UTF-8 first, but no Unicode normalization is done.
Feature values must be (finite) numbers.
This class is a low-memory alternative to DictVectorizer and
CountVectorizer, intended for large-scale (online) learning and situations
where memory is tight, e.g. when running prediction code on embedded
devices.
For an efficiency comparison of the different feature extractors, see
:ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`.
Read more in the :ref:`User Guide <feature_hashing>`.
.. versionadded:: 0.13
Parameters
----------
n_features : int, default=2**20
The number of features (columns) in the output matrices. Small numbers
of features are likely to cause hash collisions, but large numbers
will cause larger coefficient dimensions in linear learners.
input_type : str, default='dict'
Choose a string from {'dict', 'pair', 'string'}.
Either "dict" (the default) to accept dictionaries over
(feature_name, value); "pair" to accept pairs of (feature_name, value);
or "string" to accept single strings.
feature_name should be a string, while value should be a number.
In the case of "string", a value of 1 is implied.
The feature_name is hashed to find the appropriate column for the
feature. The value's sign might be flipped in the output (but see
non_negative, below).
dtype : numpy dtype, default=np.float64
The type of feature values. Passed to scipy.sparse matrix constructors
as the dtype argument. Do not set this to bool, np.boolean or any
unsigned integer type.
alternate_sign : bool, default=True
When True, an alternating sign is added to the features as to
approximately conserve the inner product in the hashed space even for
small n_features. This approach is similar to sparse random projection.
.. versionchanged:: 0.19
``alternate_sign`` replaces the now deprecated ``non_negative``
parameter.
See Also
--------
DictVectorizer : Vectorizes string-valued features using a hash table.
sklearn.preprocessing.OneHotEncoder : Handles nominal/categorical features.
Notes
-----
This estimator is :term:`stateless` and does not need to be fitted.
However, we recommend to call :meth:`fit_transform` instead of
:meth:`transform`, as parameter validation is only performed in
:meth:`fit`.
Examples
--------
>>> from sklearn.feature_extraction import FeatureHasher
>>> h = FeatureHasher(n_features=10)
>>> D = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}]
>>> f = h.transform(D)
>>> f.toarray()
array([[ 0., 0., -4., -1., 0., 0., 0., 0., 0., 2.],
[ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.]])
With `input_type="string"`, the input must be an iterable over iterables of
strings:
>>> h = FeatureHasher(n_features=8, input_type="string")
>>> raw_X = [["dog", "cat", "snake"], ["snake", "dog"], ["cat", "bird"]]
>>> f = h.transform(raw_X)
>>> f.toarray()
array([[ 0., 0., 0., -1., 0., -1., 0., 1.],
[ 0., 0., 0., -1., 0., -1., 0., 0.],
[ 0., -1., 0., 0., 0., 0., 0., 1.]])
"""
# raw_X should have been called X
__metadata_request__transform = {"raw_X": metadata_routing.UNUSED}
_parameter_constraints: dict = {
"n_features": [Interval(Integral, 1, np.iinfo(np.int32).max, closed="both")],
"input_type": [StrOptions({"dict", "pair", "string"})],
"dtype": "no_validation", # delegate to numpy
"alternate_sign": ["boolean"],
}
def __init__(
self,
n_features=(2**20),
*,
input_type="dict",
dtype=np.float64,
alternate_sign=True,
):
self.dtype = dtype
self.input_type = input_type
self.n_features = n_features
self.alternate_sign = alternate_sign
@_fit_context(prefer_skip_nested_validation=True)
def fit(self, X=None, y=None):
"""Only validates estimator's parameters.
This method allows to: (i) validate the estimator's parameters and
(ii) be consistent with the scikit-learn transformer API.
Parameters
----------
X : Ignored
Not used, present here for API consistency by convention.
y : Ignored
Not used, present here for API consistency by convention.
Returns
-------
self : object
FeatureHasher class instance.
"""
return self
def transform(self, raw_X):
"""Transform a sequence of instances to a scipy.sparse matrix.
Parameters
----------
raw_X : iterable over iterable over raw features, length = n_samples
Samples. Each sample must be iterable an (e.g., a list or tuple)
containing/generating feature names (and optionally values, see
the input_type constructor argument) which will be hashed.
raw_X need not support the len function, so it can be the result
of a generator; n_samples is determined on the fly.
Returns
-------
X : sparse matrix of shape (n_samples, n_features)
Feature matrix, for use with estimators or further transformers.
"""
raw_X = iter(raw_X)
if self.input_type == "dict":
raw_X = (_iteritems(d) for d in raw_X)
elif self.input_type == "string":
first_raw_X = next(raw_X)
if isinstance(first_raw_X, str):
raise ValueError(
"Samples can not be a single string. The input must be an iterable"
" over iterables of strings."
)
raw_X_ = chain([first_raw_X], raw_X)
raw_X = (((f, 1) for f in x) for x in raw_X_)
indices, indptr, values = _hashing_transform(
raw_X, self.n_features, self.dtype, self.alternate_sign, seed=0
)
n_samples = indptr.shape[0] - 1
if n_samples == 0:
raise ValueError("Cannot vectorize empty sequence.")
X = sp.csr_matrix(
(values, indices, indptr),
dtype=self.dtype,
shape=(n_samples, self.n_features),
)
X.sum_duplicates() # also sorts the indices
return X
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.input_tags.two_d_array = False
if self.input_type == "string":
tags.input_tags.string = True
elif self.input_type == "dict":
tags.input_tags.dict = True
return tags
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